A hybrid \(3D-2D\) -CNN architecture was presented by [1]} in which \(3D\) -CNN is first used to extract joint spectral-spatial features and then \(2D\) -CNN is further used to obtain more abstract spatial contextual features. The study [2]} proposed a Bayesian HSIC architecture that combines CNN with Markov random field. The CNN first extracts joint spectral-spatial features and then a smooth MRF prior is placed on class labels to further refine the spatial details. Convolutional neural networks are greatly affected by overfitting and vanishing gradient problems and to overcome this a separable attention network was introduced by [3]}. Where the input feature maps are divided into several groups and split them along the channel dimension and finally an attention mask encoding global contextual information by combine them together. Recently, generalized gradient centralized \(3D\)  convolution (G2C-Conv3D) was introduced in [4]} to combine both the intensity level semantic information and gradient level detailed information extracted from raw HSIs during the convolutions operation. To boost the performance of accurate land-cover types classification, G2C-Conv3D can be easily plugged into the existing HSIs feature extraction networks.
